Define your target machine to run on¶

Import ROSE main modules:

from rose.learner import ActiveLearner
from rose.engine import Task, ResourceEngine

Define your resource engine, as we described in our previous Target Resources step:

engine = ResourceEngine({'runtime': 30,
                         'resource': 'local.localhost'})
acl = ActiveLearner(engine)

Now our resource engine is defined, lets define our main AL workflow components:

Note

The Task object is based on the Radical.Pilot.TaskDescription, meaning that users can pass any args and kwargs that the Radical.Pilot.TaskDescription can accept to the Task object.

@acl.simulation_task
def simulation(*args):
    return Task(executable=f'python3 sim.py')

@acl.training_task
def training(*args):
    return Task(executable=f'python3 train.py')

@acl.active_learn_task
def active_learn(*args):
    return Task(executable=f'python3 active.py')

Optionally, you can specify a metric to monitor and act as a condition to terminate once your results reach the specified value:

Tip

Specifying both @acl.as_stop_criterion and max_iter will cause ROSE to follow whichever constraint is satisfied first. Specifying neither will cause an error and eventually a failure to your workflow.

Note

ROSE supports custom/user-defined metrics in addition to a wide range of standard metrics. For a list of standard metrics and how to define a custom metrics, please refer to the following link: Standard Metrics.

# Defining the stop criterion with a metric (MSE in this case)
@acl.as_stop_criterion(metric_name='mean_squared_error_mse', threshold=0.1)
def check_mse(*args):
    return Task(executable=f'python3 check_mse.py')

Warning

For any metric function like @acl.as_stop_criterion the invoked script like check_mse.py must return a numerical value.

Finally invoke the tasks and register them with the active learner as a workflow.

Note

In the Sequential Learner, the invocation order of the tasks does not matter as ROSE, sequential learner has a predefined order of tasks as follows: simulation --> training --> active_learn.

simul = simulation()
train = training()
active = active_learn()
stop_cond = check_mse()

# Start the teaching loop and break if max_iter = 10 or stop condition is met
acl.teach(max_iter=10)
engine.shutdown()